Published August 23, 2023 | Version 2.0
Dataset Open

Optimization procedure of low frequency vibration energy harvester based on magnetic levitation: Datasets and scripts

  • 1. Universidad Pública de Navarra

Description

****** Please view the README.txt file for detailed documentation of data. ******

 

Title: Optimization procedure of low frequency vibration energy harvester based on magnetic levitation: Datasets and scripts
Version: 2.0
Date of Release: 2023/08/23
Identifier: doi:10.5281/zenodo.8317223
Permalink: http://dx.doi.org/10.5281/zenodo.8317223


Associated publication: I. Royo-Silvestre, J. J. Beato-López, C. Gómez-Polo "Optimization procedure of low frequency vibration energy harvester  based on magnetic levitation", Applied Energy, Volume 360, 15 April 2024, 122778

Link to publication: https://doi.org/10.1016/j.apenergy.2024.122778


Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README file.

 

Contact information: Isaac Royo Silvestre, Universidad Pública de Navarra, Pamplona, Spain., isaac.royo@unavarra.es
Co-authors: juanjesus.beato@unavarra.es, gpolo@unavarra.es

 

Dates of data collection: 2023/03
Geographic location: Pamplona, Spain

 

This directory contains the following datasets and scripts:

SCRIPTS

- harvester_op.m: Matlab script to automate the design and optimize a magnetic spring based vibration energy harvester (more information in the associated paper)

- harvester_op_par.m:     Matlab script, a version of harvester_op.m modified for parallel computing and shorter execution time in multicore computers (file added in v 2.0 of the data upload).

DATASETS
- data.zip: Experimental data recorded by the datalogger as well as tabular data required to plot curves (compressed zip file) in csv format

 

Specific documentation of each file is described in readme files.

 

Refer to the original manuscript (see above) and the text of the Supplementary Materials published alongside this manuscript for additional information regarding the collection and generation of these data.

Notes

This work is funded by Spanish Agencia Estatal de Investigación and Ministerio de Ciencia e Innovación MCIN/AEI/ 10.13039/501100011033 and by "European Union NextGenerationEU/PRTR", project TED2021-130884B-I00. Open access funding provided by Universidad Pública de Navarra.

Files

data.zip

Files (13.6 MB)

Name Size Download all
md5:77b9d367ab89297c2589591687665f1a
13.5 MB Preview Download
md5:c95f130351fd26df464d516f810916a2
40.6 kB Download
md5:1d6aa6de17f3129187f241a4e5d99110
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md5:f593c2a45c48ac0555af62d33468f7d0
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Additional details

Related works

Is published in
Journal article: 10.1016/j.apenergy.2024.122778 (DOI)
Is referenced by
Output management plan: 10.5281/zenodo.14937106 (DOI)